Assessing MapReduce for Internet Computing: A Comparison of Hadoop and BitDew-MapReduce

MapReduce is emerging as an important programming model for data-intensive application. Adapting this model to desktop grid would allow taking advantage of the vast amount of computing power and distributed storage to execute new range of application able to process enormous amount of data. In 2010, we have presented the first implementation of MapReduce dedicated to Internet Desktop Grid based on the BitDew middleware. In this paper, we present new optimizations to BitDew-MapReduce (BitDew-MR): aggressive task backup, intermediate result backup, task re-execution mitigation and network failure hiding. We propose a new experimental framework which emulates key fundamental aspects of Internet Desktop Grid. Using the framework, we compare BitDew-MR and the open-source Hadoop middleware on Grid5000. Our experimental results show that 1) BitDew-MR successfully passes all the stress-tests of the framework while Hadoop is unable to work in typical wide-area network topology which includes PC hidden behind firewall and NAT; 2) BitDew-MR outperforms Hadoop performances on several aspects: scalability, fairness, resilience to node failures, and network disconnections.